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Viewing page 6 out of 12 pages
Viewing questions 51-60 out of questions
Questions # 51:

You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.

You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)

Options:

A.

Redis

B.

HBase

C.

MySQL

D.

MongoDB

E.

Cassandra

F.

HDFS with Hive

Questions # 52:

Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

Options:

A.

The CSV data loaded in BigQuery is not flagged as CSV.

B.

The CSV data has invalid rows that were skipped on import.

C.

The CSV data loaded in BigQuery is not using BigQuery’s default encoding.

D.

The CSV data has not gone through an ETL phase before loading into BigQuery.

Questions # 53:

You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?

Options:

A.

Change the processing job to use Google Cloud Dataproc instead.

B.

Manually start the Cloud Dataflow job each morning when you get into the office.

C.

Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.

D.

Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.

Questions # 54:

Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.

You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

Options:

A.

Introduce data compression for each file to increase the rate file of file transfer.

B.

Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

C.

Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

D.

Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.

E.

Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.

Questions # 55:

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

Options:

A.

Load the data every 30 minutes into a new partitioned table in BigQuery.

B.

Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

C.

Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

D.

Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

Questions # 56:

You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity ‘Movie’ the property ‘actors’ and the property ‘tags’ have multiple values but the property ‘date released’ does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

Question # 56

Question # 56

Options:

A.

Option A

B.

Option B.

C.

Option C

D.

Option D

Questions # 57:

You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:

    The user profile: What the user likes and doesn’t like to eat

    The user account information: Name, address, preferred meal times

    The order information: When orders are made, from where, to whom

The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?

Options:

A.

BigQuery

B.

Cloud SQL

C.

Cloud Bigtable

D.

Cloud Datastore

Questions # 58:

An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.

They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

Options:

A.

BigQuery

B.

Cloud SQL

C.

Cloud BigTable

D.

Cloud Datastore

Questions # 59:

You need to set access to BigQuery for different departments within your company. Your solution should comply with the following requirements:

    Each department should have access only to their data.

    Each department will have one or more leads who need to be able to create and update tables and provide them to their team.

    Each department has data analysts who need to be able to query but not modify data.

How should you set access to the data in BigQuery?

Options:

A.

Create a dataset for each department. Assign the department leads the role of OWNER, and assign the data analysts the role of WRITER on their dataset.

B.

Create a dataset for each department. Assign the department leads the role of WRITER, and assign the data analysts the role of READER on their dataset.

C.

Create a table for each department. Assign the department leads the role of Owner, and assign the data analysts the role of Editor on the project the table is in.

D.

Create a table for each department. Assign the department leads the role of Editor, and assign the data analysts the role of Viewer on the project the table is in.

Questions # 60:

Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?

Options:

A.

Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.

B.

Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.

C.

Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.

D.

Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull

subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in

the bid event that is processed first.

Viewing page 6 out of 12 pages
Viewing questions 51-60 out of questions
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